Data Science Blog

Month: March 2017

We present our latest work on the CrowdTruth framework, titled “Human Computing for the Real World”, at the ICT Open 2017 conference on 21st and 22nd of March 2017. I made a new video that demonstrates the different aspects of the framework for dealing with ambiguity in data, crowdsourcing of human interpretations, and evaluating disagreement between annotations.

Our ControCurator paper abstract titled “ControCurator: Understanding Controversy Using Collective Intelligence” has been accepted at Collective Intelligence 2017. In this paper we describe the aspects of controversy: the time-persistence, emotion, multiple actors, polarity and openness. Using crowdsourcing, the ControCurator dataset of 31888 controversy annotations was obtained for the relevance of these aspects to 5048 Guardian articles. The results indicate that each of these aspects is a positive indicator of controversy, but also that there is a clear difference in their signal strength. Most notably, the emotion was found to be the highest indicator. Though, all the measured controversy aspects were found to positively correlate with controversy. These results suggest that the controversy model is accurate and useful for modeling controversy in news articles.

Our demo of ControCurator titled “ControCurator: Human-Machine Framework For Identifying Controversy” will be shown at ICT Open 2017. In this demo the ControCurator human-machine framework for identifying controversy in multimodal data is shown. The goal of ControCurator is to enable modern information access systems to discover and understand controversial topics and events by bringing together crowds and machines in a joint active learning workflow for the creation of adequate training data. This active learning workflow allows a user to identify and understand controversy in ongoing issues, regardless of whether there is existing knowledge on the topic.